Artificial intelligence and sustainability: Redefining global practices for a better future

Artificial Intelligence (AI) has become a revolutionary phenomenon in achieving sustainability, but the key question is: How can AI be systematically leveraged to reinvent the world’s practices in a more sustainable and a better future? The focus of this paper is to research the intersection point of AI, digital transformation, and sustainable systems, address the real-world use and governance structures. The adopted methodology is a mixed methods approach, in which a review of the current literature AI driven sustainability initiatives will be followed by case studies’ analysis of industries such as supply chain management, smart agriculture and green manufacturing. An overview of AI focuses on these aspects and provides comparative analysis among the examples of their use to identify opportunities and challenges in implementing sustainability into strategies of technical modernization. The most important results include that the AI allows achieving enormous efficiency improvements by means of predictive analytics, resource optimization, and automation, which results in quantifiable carbon footprints and operational expenses. An example of AI-based supply chain optimization is waste reduction and resilience, which are achieved through AI-based supply chain optimization solutions, and smart agriculture applications, which are used to forecast yields and improve environmental impact. Nevertheless, the study also reveals weaknesses in ethical governance, data transparency, and fair access, which should be overcome to make sure that AI can positively impact the global sustainability objectives. The conclusion highlights the fact that AI is not a technology facilitator but a tactical co-creator for sustainable futures. The point is quite obvious: the adoption of AI as a responsible practice that is regulated by ethical standards and shared governance is needed to strike a balance between innovation and ecological and social responsibility. AI is here to sustain and outlive all other technologies so far for the achievement of the United Nations Sustainable Development Goals (SDGs). The present paper provides practical implications to policy makers, industry players, and scientists interested in using AI to create sustainable practices in the world.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-04-10
DOI
https://doi.org/10.1051/shsconf/202623004001/pdf
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial intelligence and sustainability: Redefining global practices for a better future

Anamika Singh, Anant Deogaonkar
Springer Link (Chiba Institute of Technology)
Digital Transformation in Industry
article

Artificial intelligence and sustainability: Redefining global practices for a better future

Anamika Singh, Anant Deogaonkar
article en

Abstract

Artificial Intelligence (AI) has become a revolutionary phenomenon in achieving sustainability, but the key question is: How can AI be systematically leveraged to reinvent the world’s practices in a more sustainable and a better future? The focus of this paper is to research the intersection point of AI, digital transformation, and sustainable systems, address the real-world use and governance structures. The adopted methodology is a mixed methods approach, in which a review of the current literature AI driven sustainability initiatives will be followed by case studies’ analysis of industries such as supply chain management, smart agriculture and green manufacturing. An overview of AI focuses on these aspects and provides comparative analysis among the examples of their use to identify opportunities and challenges in implementing sustainability into strategies of technical modernization. The most important results include that the AI allows achieving enormous efficiency improvements by means of predictive analytics, resource optimization, and automation, which results in quantifiable carbon footprints and operational expenses. An example of AI-based supply chain optimization is waste reduction and resilience, which are achieved through AI-based supply chain optimization solutions, and smart agriculture applications, which are used to forecast yields and improve environmental impact. Nevertheless, the study also reveals weaknesses in ethical governance, data transparency, and fair access, which should be overcome to make sure that AI can positively impact the global sustainability objectives. The conclusion highlights the fact that AI is not a technology facilitator but a tactical co-creator for sustainable futures. The point is quite obvious: the adoption of AI as a responsible practice that is regulated by ethical standards and shared governance is needed to strike a balance between innovation and ecological and social responsibility. AI is here to sustain and outlive all other technologies so far for the achievement of the United Nations Sustainable Development Goals (SDGs). The present paper provides practical implications to policy makers, industry players, and scientists interested in using AI to create sustainable practices in the world.

Springer Link (Chiba Institute of Technology)
Zero hunger
Openalex Percentile: Top 17%
Digital Transformation in Industry
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Artificial intelligence and sustainability: Redefining global practices for a better future — Anamika Singh, Anant Deogaonkar · Springer Link (Chiba Institute of Technology) (2026) | TGRS Research Map | TGRS